Physicians have many questions when seeing patients. Primary care physicians are reported to generate between 0.7 and 18.5 questions for every 10 patient visits. The published medical literature is an important resource helping physicians to access up-to-date clinical information and thereby to enhance the quality of patient care. For example, the case study in the above example (i.e., diagnostic procedures and treatment for cellulites) was published in a "Clinical Practice" article in the New England Journal of Medicine (NEJM). Although PubMed is frequently used by physicians in large hospitals, it does not return answers to specific questions. Frequently, PubMed returns a large number of articles in response to a specific user query. Physicians have limited time for browsing the articles retrieved;it has been found that physicians spend on average two minutes or less seeking an answer to a question, and that if a search takes longer it is likely to be abandoned. An evaluation study has shown that it takes an average of more than 30 minutes for a healthcare provider to search for answer from PubMed, which makes "information seeking ... practical only `after hours'and not in the clinical setting." It has been concluded that a lack of time is the most common obstacle resulting in many unanswered medical questions. The importance of answering physicians'questions at the point of patient care has been widely recognized by the medical community. Many medical databases (e.g., UpToDate and Thomson MICROMEDEX) provide summaries to answer important medical questions related to patient care. However, most of the summaries are written by medical experts who manually review the literature information. The databases are limited in their scope and timeliness. We hypothesize that we can develop medical language processing (MLP) approaches to build a fully automated system HERMES - Help physicians to Extract and aRticulate Multimedia information from literature to answer their ad-hoc medical quEstionS. HERMES will automatically retrieve, extract, analyze, and integrate text, image, and video from the literature and formulate them as answers to ad-hoc medical questions posed by physicians. Our preliminary results show that even a limited HERMES working system outperformed other information retrieval systems and can generate answers within a timeframe necessary to meet the demands of physicians. HERMES promise to assist physicians for practicing evidence-based medicine (EBM), the medical practice that involves the explicit use of current best evidence, i.e., high-quality patient-centered clinical research reported in the primary medical literature. Our specific aims are: 1) Identify information needs from ad-hoc medical questions. We will incorporate rich semantic, statistical, and machine learning approaches to map ad-hoc medical questions to their component question types automatically. A component question type is a generic, simple question type that requires an answer strategy that is different from other component question types. 2) Develop new information retrieval models that integrate domain-specific knowledge for retrieving relevant documents in response to an ad-hoc medical question. 3) Extract relevant text, images, and videos from the retrieved documents in response to an ad-hoc medical question. 4) Integrate text, images, and videos, fusing information to generate a short and coherent multimedia summary. 5) Design a usability study to measure efficacy, accuracy and perceived ease of use of HERMES and to compare HERMES with other information systems.